Advances in Data-Driven Wind Turbine Condition Monitoring
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Systems & Control Engineering".
Deadline for manuscript submissions: closed (15 August 2024) | Viewed by 3748
Special Issue Editor
Interests: wind turbines; condition monitoring; fault diagnosis; non-stationary machinery; control and monitoring; vibrations; applied statistics; numerical modelling; mechanical systems dynamics
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In the near future, wind turbines will be the leading form of renewable energy technology worldwide. In light of this development, it is fundamental to minimize the costs of this technology to the greatest degree possible, and this will involve, in particular, reducing the operation and maintenance (O&M) costs, which represent the largest proportion of the costs of a wind farm. Therefore, intelligent methods for the proper diagnosis of faults and more efficient management of wind farms are at the center of the scientific literature on wind energy.
In this context, the importance of the data science methods applied to wind turbine condition monitoring and fault diagnosis has grown in recent years. Several types of data are frequently employed for this purpose, and the techniques are selected based on the data sampling time (ranging from ten minutes for SCADA-collected data to milliseconds or less for accelerometer-collected data) and on the component to be monitored.
On this basis, the objective of this Special Issue is to collect high-quality contributions about all aspects of data-driven wind turbine condition monitoring and fault diagnosis. Contributions addressing the following topics are particularly welcome, though other themes will also be considered:
- Condition monitoring;
- Fault diagnosis;
- Prognostics;
- SCADA data analysis;
- Vibration analysis;
- Signal processing;
- Machine learning;
- Deep learning;
- Explainable artificial intelligence (XAI);
- Normal behavior models;
- Regression;
- Classification;
- Feature selection;
- Multivariate analysis;
- Pattern recognition;
- Physics-based modelling;
- Digital twins;
- Gears and bearings diagnostics;
- PMS generators;
- Blade pitch systems;
- Yaw error;
- Power electronics;
- Structural health monitoring;
- Wind turbine power curves;
- Wind turbine control;
- Wind turbine under-performance;
- Performance analytics and control;
- Wind turbine life cycle assessment;
- Wind turbine ageing and end-of-life issues.
Dr. Davide Astolfi
Guest Editor
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